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Bayesian estimation of the global minimum variance portfolio

Bodnar, Taras; Mazur, Stepan LU and Okhrin, Yarema (2017) In European Journal of Operational Research 256(1). p.292-307
Abstract

In this paper we consider the estimation of the weights of optimal portfolios from the Bayesian point of view under the assumption that the conditional distributions of the logarithmic returns are normal. Using the standard priors for the mean vector and the covariance matrix, we derive the posterior distributions for the weights of the global minimum variance portfolio. Moreover, we reparameterize the model to allow informative and non-informative priors directly for the weights of the global minimum variance portfolio. The posterior distributions of the portfolio weights are derived in explicit form for almost all models. The models are compared by using the coverage probabilities of credible intervals. In an empirical study we... (More)

In this paper we consider the estimation of the weights of optimal portfolios from the Bayesian point of view under the assumption that the conditional distributions of the logarithmic returns are normal. Using the standard priors for the mean vector and the covariance matrix, we derive the posterior distributions for the weights of the global minimum variance portfolio. Moreover, we reparameterize the model to allow informative and non-informative priors directly for the weights of the global minimum variance portfolio. The posterior distributions of the portfolio weights are derived in explicit form for almost all models. The models are compared by using the coverage probabilities of credible intervals. In an empirical study we analyze the posterior densities of the weights of an international portfolio.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Credible interval, Global minimum variance portfolio, Posterior distribution, Wishart distribution
in
European Journal of Operational Research
volume
256
issue
1
pages
16 pages
publisher
Elsevier
external identifiers
  • scopus:84990243844
  • wos:000384854500027
ISSN
0377-2217
DOI
10.1016/j.ejor.2016.05.044
language
English
LU publication?
yes
id
41aa578c-2044-42b0-a80b-816517409cc1
date added to LUP
2016-10-19 09:30:10
date last changed
2018-05-20 04:27:53
@article{41aa578c-2044-42b0-a80b-816517409cc1,
  abstract     = {<p>In this paper we consider the estimation of the weights of optimal portfolios from the Bayesian point of view under the assumption that the conditional distributions of the logarithmic returns are normal. Using the standard priors for the mean vector and the covariance matrix, we derive the posterior distributions for the weights of the global minimum variance portfolio. Moreover, we reparameterize the model to allow informative and non-informative priors directly for the weights of the global minimum variance portfolio. The posterior distributions of the portfolio weights are derived in explicit form for almost all models. The models are compared by using the coverage probabilities of credible intervals. In an empirical study we analyze the posterior densities of the weights of an international portfolio.</p>},
  author       = {Bodnar, Taras and Mazur, Stepan and Okhrin, Yarema},
  issn         = {0377-2217},
  keyword      = {Credible interval,Global minimum variance portfolio,Posterior distribution,Wishart distribution},
  language     = {eng},
  month        = {01},
  number       = {1},
  pages        = {292--307},
  publisher    = {Elsevier},
  series       = {European Journal of Operational Research},
  title        = {Bayesian estimation of the global minimum variance portfolio},
  url          = {http://dx.doi.org/10.1016/j.ejor.2016.05.044},
  volume       = {256},
  year         = {2017},
}